5 Best Ways to Return the Cumulative Sum of Array Elements Over Axis 1 Treating NaNs as Zero in Python

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[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.

💡 Problem Formulation: In data analysis, dealing with missing values is a common problem. Specifically, when computing the cumulative sum across a particular axis of an array, NaNs (Not a Number values) can pose a challenge. The aim is to efficiently compute the cumulative sum over axis 1, interpreting NaNs as zeros in Python. For instance, given an input array [[1, NaN, 3], [NaN, 5, NaN], [7, 8, NaN]], the desired output for the cumulative sum over axis 1 would be [[1, 1, 4], [0, 5, 5], [7, 15, 15]].

Method 1: Using NumPy with NaN Handling

This method involves utilizing NumPy’s nan_to_num function to convert NaNs to zeros and then calculating the cumulative sum using cumsum. NumPy is a powerful library for numerical computations, and it provides specialized functions for handling NaN values.

Here’s an example:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Replace NaNs with zeros and compute cumulative sum over axis 1
cumulative_sum = np.nan_to_num(array_with_nans).cumsum(axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet first replaces all NaN values in the array with 0 using np.nan_to_num. With NaNs treated as zeros, applying cumsum with axis=1 computes the cumulative sum along the horizontal axis, providing the desired result.

Method 2: Using Pandas DataFrame

Pandas DataFrames have built-in methods for handling NaNs directly. By converting the array into a DataFrame, we can use the fillna method to replace NaNs with zeros and then apply the cumsum method to obtain the cumulative sum over the desired axis.

Here’s an example:

import pandas as pd
import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Convert to DataFrame, replace NaNs with zeros and compute the cumulative sum over axis 1
df = pd.DataFrame(array_with_nans)
cumulative_sum = df.fillna(0).cumsum(axis=1)

print(cumulative_sum)

Output:

     0     1     2
0  1.0   1.0   4.0
1  0.0   5.0   5.0
2  7.0  15.0  15.0

By converting the initial array to a DataFrame, NaN values are naturally handled. The fillna(0) method is then applied, replacing NaNs with zeros, after which cumsum(axis=1) computes the cumulative sum horizontally. Pandas provides a more extensive data manipulation toolkit compared to pure NumPy.

Method 3: Masking NaNs with NumPy cumsum and where

Another approach with NumPy is to use np.where to selectively replace NaNs with 0 directly within the cumulative sum computation process, achieving the desired result in a single line without altering the original array.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Compute cumulative sum over axis 1, directly handling NaNs
cumulative_sum = np.cumsum(np.where(np.isnan(array_with_nans), 0, array_with_nans), axis=1)

print(cumulative_sum)

Output:

[[ 1.  1.  4.]
 [ 0.  5.  5.]
 [ 7. 15. 15.]]

This code snippet uses np.where to create a modified array where NaNs are replaced with zeros. The np.cumsum function is then directly applied to this masked array to calculate the cumulative sum over axis 1. This method avoids modifying the original array and is concise.

Method 4: Manual Iteration and Summation

For environments where NumPy is not available, a manual iteration approach can be adopted. This method involves iterating through the array, treating NaNs as zeros, and manually calculating the cumulative sum.

Here’s an example:

import math

# Define the array with NaNs and prepare an output array with the same shape
array_with_nans = [[1, float('nan'), 3], [float('nan'), 5, float('nan')], [7, 8, float('nan')]]
cumulative_sum = [[0 for _ in range(len(row))] for row in array_with_nans]

# Manually compute the cumulative sum over axis 1, treating NaNs as zero
for i, row in enumerate(array_with_nans):
    for j, val in enumerate(row):
        cumulative_sum[i][j] = cumulative_sum[i][j-1] + (val if not math.isnan(val) else 0)

print(cumulative_sum)

Output:

[[1, 1, 4], [0, 5, 5], [7, 15, 15]]

In this snippet, we define a new array with the same dimensions as our input array to hold the cumulative sum results. The outer loop iterates through each row and the inner loop goes through each element. We add the value if it’s not NaN—otherwise, we add zero. This method does not require external libraries, which can be beneficial when working in restricted environments.

Bonus One-Liner Method 5: List Comprehension with NumPy

A bonus method showcases the power of Python’s list comprehension when used in conjunction with NumPy functions. This one-liner approach enables concise code while achieving the same result.

Here’s an example:

import numpy as np

# Define the array with NaNs
array_with_nans = np.array([[1, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]])

# Use list comprehension to replace NaNs and compute cumulative sum over axis 1
cumulative_sum = np.array([np.cumsum([0 if np.isnan(i) else i for i in row]) for row in array_with_nans])

print(cumulative_sum)

Output:

[[ 1  1  4]
 [ 0  5  5]
 [ 7 15 15]]

This one-liner uses a list comprehension to traverse each row and replace NaNs with zeros inline before calculating the cumulative sum with np.cumsum. It combines the functionality of NumPy and Python’s compact syntax to provide a quick and readable solution.

Summary/Discussion

  • Method 1: NumPy with NaN handling. Strengths: Efficient and direct. Weaknesses: Requires NumPy, which might not be available in all environments.
  • Method 2: Pandas DataFrame. Strengths: Works well with larger, more complex data operations. Weaknesses: Overhead of converting to DataFrame; more suited for tabular data than numerical arrays.
  • Method 3: NumPy cumsum and where. Strengths: One-liner and doesn’t modify the original array. Weaknesses: Still requires NumPy; less intuitive than other methods.
  • Method 4: Manual Iteration. Strengths: Does not require external libraries; useful in restricted environments. Weaknesses: Less efficient and more verbose.
  • Method 5: List Comprehension with NumPy. Strengths: Concise and Pythonic. Weaknesses: Requires understanding of list comprehensions and NumPy.